{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Dataflowr](https://raw.githubusercontent.com/dataflowr/website/master/_assets/dataflowr_logo.png)](https://dataflowr.github.io/website/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalizing flows\n",
    "\n",
    "The image below is taken from this very good blog post on normalizing flows : [blogpost](https://lilianweng.github.io/lil-log/2018/10/13/flow-based-deep-generative-models.html)\n",
    "\n",
    "![](https://raw.githubusercontent.com/dataflowr/website/master/modules/extras/flows/three-generative-models.png)\n",
    "\n",
    "A **flow-based generative model** is constructed by a sequence of **invertible** transformations. The main advantage of flows is that the model explicitly learns the data distribution $p(\\mathbf{x})$ and therefore the loss function is simply the negative log-likelihood.\n",
    "\n",
    "Given a sample $\\mathbf{x}$ and a prior $p(\\mathbf{z})$, we compute $f(\\mathbf{x}) = \\mathbf{z}$ with an invertible function $f$ that will be learned. Given $f$ and the prior $p(\\mathbf{z})$, we can compute the evidence $p(\\mathbf{x})$ thanks to the change of variable formula:\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\mathbf{z} &\\sim p(\\mathbf{z}), \\mathbf{z} = f(\\mathbf{x}), \\\\\n",
    "p(\\mathbf{x}) \n",
    "&= p(\\mathbf{z}) \\left\\vert \\det \\dfrac{d \\mathbf{z}}{d \\mathbf{x}} \\right\\vert  \n",
    "= p(f(\\mathbf{x})) \\left\\vert \\det \\dfrac{\\partial f(\\mathbf{x})}{\\partial \\mathbf{x}} \\right\\vert\n",
    "\\end{align*}\n",
    "$$\n",
    "where $\\dfrac{\\partial f(\\mathbf{x})}{\\partial \\mathbf{x}}$ is the Jacobian matrix of $f$.\n",
    "Recall that given a function mapping a $n$-dimensional input vector $\\mathbf{x}$ to a $m$-dimensional output vector, $f: \\mathbb{R}^n \\mapsto \\mathbb{R}^m$, the matrix of all first-order partial derivatives of this function is called the **Jacobian matrix**, $J_f$ where one entry on the i-th row and j-th column is $(J_f(\\mathbf{x}))_{ij} = \\frac{\\partial f_i(\\mathbf{x})}{\\partial x_j}$:\n",
    "$$\n",
    "{J_f(\\mathbf{x})} = \\begin{bmatrix}\n",
    "\\frac{\\partial f_1(\\mathbf{x})}{\\partial x_1} & \\dots & \\frac{\\partial f_1(\\mathbf{x})}{\\partial x_n} \\\\[6pt]\n",
    "\\vdots & \\ddots & \\vdots \\\\[6pt]\n",
    "\\frac{\\partial f_m(\\mathbf{x})}{\\partial x_1} & \\dots & \\frac{\\partial f_m(\\mathbf{x})}{\\partial x_n} \\\\[6pt]\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "Below, we will parametrize $f$ with a neural network and learn $f$ by maximizing $\\ln p(\\mathbf{x})$. More precisely, given a dataset $(\\mathbf{x}_1,\\dots,\\mathbf{x}_n)$ and a model provided by a prior $p(\\mathbf{z})$ and a neural network $f$, we optimize the weights of $f$ by minimizing:\n",
    "$$\n",
    "-\\sum_{i}\\ln p(\\mathbf{x_i}) = \\sum_i -\\ln p(f(\\mathbf{x}_i)) -\\ln\\left\\vert \\det \\dfrac{\\partial f(\\mathbf{x}_i)}{\\partial \\mathbf{x}} \\right\\vert.\n",
    "$$\n",
    "\n",
    "**We need to ensure that $f$ is always invertible and that the determinant is simple to compute.**\n",
    "\n",
    "## [Density estimation using Real NVP](https://arxiv.org/abs/1605.08803) \n",
    "by Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio (2016)\n",
    "\n",
    "[Real NVP](https://arxiv.org/abs/1605.08803) uses function $f$ obtained by stacking affine coupling layers which for an input $\\mathbf{x}\\in \\mathbb{R}^D$ produce the output $\\mathbf{y}\\in\\mathbb{R}^D$ defined by ($d<D$): \n",
    "$$\n",
    "\\begin{align}\n",
    "\\label{eq:aff}\n",
    "\\mathbf{y}_{1:d} &= \\mathbf{x}_{1:d}\\\\\n",
    "\\mathbf{y}_{d+1:D} &= \\mathbf{x}_{d+1:D} \\odot \\exp\\left(s(\\mathbf{x}_{1:d})\\right) +t(\\mathbf{x}_{1:d}) ,\n",
    "\\end{align}\n",
    "$$\n",
    "where $s$ (scale) and $t$ (translation) are neural networks mapping $\\mathbb{R}^d$ to $\\mathbb{R}^{D-d}$ and $\\odot$ is the element-wise product.\n",
    "\n",
    "For any functions $s$ and $t$, the affine coupling layer is invertible:\n",
    "\\begin{align*}\n",
    "\\begin{cases}\n",
    "\\mathbf{y}_{1:d} &= \\mathbf{x}_{1:d} \\\\ \n",
    "\\mathbf{y}_{d+1:D} &= \\mathbf{x}_{d+1:D} \\odot \\exp({s(\\mathbf{x}_{1:d})}) + t(\\mathbf{x}_{1:d})\n",
    "\\end{cases}\n",
    "\\Leftrightarrow \n",
    "\\begin{cases}\n",
    "\\mathbf{x}_{1:d} &= \\mathbf{y}_{1:d} \\\\ \n",
    "\\mathbf{x}_{d+1:D} &= (\\mathbf{y}_{d+1:D} - t(\\mathbf{y}_{1:d})) \\odot \\exp(-s(\\mathbf{y}_{1:d}))\n",
    "\\end{cases}\n",
    "\\end{align*}\n",
    "\n",
    "The Jacobian of an affine coupling layer is a lower triangular matrix:\n",
    "\\begin{align*}\n",
    "J(\\mathbf{x}) =  \\frac{\\partial \\mathbf{y}}{\\partial \\mathbf{x}}=\n",
    "\\begin{bmatrix}\n",
    "  \\mathbb{I}_d & \\mathbf{0}_{d\\times(D-d)} \\\\[5pt]\n",
    "  \\frac{\\partial \\mathbf{y}_{d+1:D}}{\\partial \\mathbf{x}_{1:d}} & \\text{diag}(\\exp(s(\\mathbf{x}_{1:d})))\n",
    "\\end{bmatrix}\n",
    "\\end{align*}\n",
    "Hence the determinant is simply the product of terms on the diagonal:\n",
    "\\begin{align*}\n",
    "\\left\\vert\\det(J(\\mathbf{x}))\\right\\vert\n",
    "= \\prod_{j=1}^{D-d}\\exp(s(\\mathbf{x}_{1:d}))_j\n",
    "= \\exp\\left(\\sum_{j=1}^{D-d} s(\\mathbf{x}_{1:d})_j\\right)\n",
    "\\end{align*}\n",
    "Note that, we do not need to compute the Jacobian of $s$ or $t$ and to compute $f^{-1}$, we do not need to compute the inverse of $s$ or $t$ (which might not exist!). In other words, we can take arbitrary complex functions for $s$ and $t$.\n",
    "\n",
    "In one affine coupling layer, some dimensions (channels) remain unchanged. To make sure all the inputs have a chance to be altered, the model reverses the ordering in each layer so that different components are left unchanged. Following such an alternating pattern, the set of units which remain identical in one transformation layer are always modified in the next. \n",
    "\n",
    "This can be implemented with binary masks. First, we can extend the scale and neural networks to mappings form $\\mathbb{R}^D$ to $\\mathbb{R}^D$. Then taking a mask $\\mathbf{b} = (1,\\dots,1,0,\\dots,0)$ with $d$ ones, so that we have for the affine layer:\n",
    "\\begin{align*}\n",
    "\\mathbf{y} = \\mathbf{x} \\odot \\exp\\big((1-\\mathbf{b}) \\odot s(\\mathbf{b} \\odot \\mathbf{x})\\big) + (1-\\mathbf{b}) \\odot t(\\mathbf{b} \\odot \\mathbf{x}).\n",
    "\\end{align*}\n",
    "Note that we have\n",
    "\\begin{align*}\n",
    "\\ln \\left\\vert\\det(J(\\mathbf{x}))\\right\\vert = \\sum_{j=1}^{D} \\Big((1-\\mathbf{b})\\odot s(\\mathbf{b} \\odot \\mathbf{x})\\Big)_j,\n",
    "\\end{align*}\n",
    "and to invert the affine layer:\n",
    "\\begin{align*}\n",
    "\\mathbf{x} = \\left( \\mathbf{y} -(1-\\mathbf{b}) \\odot t(\\mathbf{b} \\odot \\mathbf{y})\\right)\\odot \\exp\\left( -(1-\\mathbf{b}) \\odot s(\\mathbf{b} \\odot \\mathbf{y})\\right)\n",
    "\\end{align*}\n",
    "Now we alternates the binary mask $\\mathbf{b}$ from one coupling layer to the other. \n",
    "\n",
    "Note, that the formula given in the paper is sligthly different:\n",
    "$$\\mathbf{y} = \\mathbf{b} \\odot \\mathbf{x} + (1 - \\mathbf{b}) \\odot \\Big(\\mathbf{x} \\odot \\exp\\big(s(\\mathbf{b} \\odot \\mathbf{x})\\big) + t(\\mathbf{b} \\odot \\mathbf{x})\\Big),$$\n",
    "but the 2 formulas give the same result!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NgMBEYdwbTS2"
   },
   "source": [
    "# Implementation of Real NVP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "loxXeNy9bTS3"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 5, 4\n",
    "rcParams['figure.dpi'] = 150\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import distributions\n",
    "from torch.nn.parameter import Parameter\n",
    "\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iFaqlfn3HcOV"
   },
   "outputs": [],
   "source": [
    "# here the scaling and translation neural networks are defined:\n",
    "nets = lambda: nn.Sequential(nn.Linear(2, 256), nn.LeakyReLU(), nn.Linear(256, 256), nn.LeakyReLU(), nn.Linear(256, 2), nn.Tanh())\n",
    "nett = lambda: nn.Sequential(nn.Linear(2, 256), nn.LeakyReLU(), nn.Linear(256, 256), nn.LeakyReLU(), nn.Linear(256, 2))\n",
    "# functions that take no arguments and return a pytorch model, dim(X) -> dim(X)\n",
    "\n",
    "masks = torch.from_numpy(np.array([[0, 1], [1, 0], [0, 1], [1, 0], [0, 1], [1, 0]]).astype(np.float32))\n",
    "# torch.Tensor of size number_of_coupling_layers x dim(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([6, 2])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masks.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the simple version with a Gaussian prior, please have a look at this [notebook](https://github.com/dataflowr/notebooks/blob/master/Module9/Normalizing_flows_sol.ipynb). Below, we consider a prior which is a mixture of Gaussians in order to do clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "id": "nFVr-ttwHcOX",
    "outputId": "cc585ee6-2740-487b-96fd-919507c45b5f"
   },
   "outputs": [],
   "source": [
    "from torch import distributions\n",
    "mix = distributions.Categorical(torch.ones(2))\n",
    "gau = distributions.MultivariateNormal(torch.eye(2), 0.05*torch.eye(2))\n",
    "prior = distributions.mixture_same_family.MixtureSameFamily(mix, gau) #distributions.MultivariateNormal(torch.tensor([0.,1.]), torch.eye(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f76759a01c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = prior.component_distribution.sample((1000,))\n",
    "\n",
    "plt.scatter(z[:,0, 0], z[:,0, 1], c='b')\n",
    "plt.scatter(z[:,1, 0], z[:,1, 1], c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "id": "nFVr-ttwHcOX",
    "outputId": "cc585ee6-2740-487b-96fd-919507c45b5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-8.8421)\n",
      "tensor([[ 0.2768,  1.4799],\n",
      "        [ 0.1518,  0.5643],\n",
      "        [ 1.3545, -0.0584]])\n"
     ]
    }
   ],
   "source": [
    "# you can compute logprob and sample from your distribution:\n",
    "print(prior.log_prob(torch.Tensor([0,0])))\n",
    "print(prior.sample((3,)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GpyZ5AqObTS7"
   },
   "outputs": [],
   "source": [
    "class RealNVP(nn.Module):\n",
    "    def __init__(self, nets, nett, mask, prior):\n",
    "        super(RealNVP, self).__init__()\n",
    "        # Create a flow\n",
    "        # nets:  a function that returns a PyTorch neural network, e.g., nn.Sequential, s = nets(), s: dim(X) -> dim(X)\n",
    "        # nett:  a function that returns a PyTorch neural network, e.g., nn.Sequential, t = nett(), t: dim(X) -> dim(X)\n",
    "        # mask:  a torch.Tensor of size #number_of_coupling_layers x #dim(X)\n",
    "        # prior: an object from torch.distributions e.g., torch.distributions.MultivariateNormal\n",
    "        self.prior = prior\n",
    "        self.mask = mask\n",
    "        self.t = torch.nn.ModuleList([nett() for _ in range(len(mask))])\n",
    "        self.s = torch.nn.ModuleList([nets() for _ in range(len(mask))])\n",
    "\n",
    "    def f(self, x):        \n",
    "        # Compute f(x) = z and log_det_Jacobian of f, \n",
    "        #    where self.mask[i], self.t[i], self.s[i] define a i-th masked coupling layer   \n",
    "        # x: a torch.Tensor, of shape batchSize x dim(X), is a datapoint\n",
    "        # return z: a torch.Tensor of shape batchSize x dim(X), a hidden representations\n",
    "        # return log_det_J: a torch.Tensor of len batchSize\n",
    "        log_det_J, z = x.new_zeros(x.shape[0]), x\n",
    "        for i in range(len(self.t)):\n",
    "            z_ = z*self.mask[i]\n",
    "            s = self.s[i](z_)*(1-self.mask[i])\n",
    "            t = self.t[i](z_)*(1-self.mask[i])\n",
    "            z = z * torch.exp(s) + t\n",
    "            log_det_J += s.sum(dim=1)\n",
    "        \n",
    "        return z, log_det_J\n",
    "    \n",
    "    def log_prob(self, x):\n",
    "        # Compute and return log p(x)\n",
    "        # using the change of variable formula and log_det_J computed by f\n",
    "        # return logp: torch.Tensor of len batchSize\n",
    "        z, logdet = self.f(x)\n",
    "        return self.prior.log_prob(z) + logdet\n",
    "        \n",
    "    def g(self, z):\n",
    "        # Compute and return g(z) = x, \n",
    "        #    where self.mask[i], self.t[i], self.s[i] define a i-th masked coupling layer   \n",
    "        # z: a torch.Tensor of shape batchSize x dim(X)\n",
    "        # return x: a torch.Tensor of shape batchSize x dim(X)\n",
    "        x = z\n",
    "        for i in reversed(range(len(self.t))):\n",
    "            x_ = self.mask[i] * x\n",
    "            s = self.s[i](x_) * (1-self.mask[i])\n",
    "            t = self.t[i](x_) * (1-self.mask[i])\n",
    "            x = (x-t) * torch.exp(-s)\n",
    "        return x\n",
    "    \n",
    "    def sample(self, batchSize): \n",
    "        # Draw and return batchSize samples from flow using implementation of g\n",
    "        # return x: torch.Tensor of shape batchSize x dim(X)\n",
    "        z = self.prior.sample((batchSize,))\n",
    "        x = self.g(z)\n",
    "        return x\n",
    "    \n",
    "    def sample_cond(self,batchSize):\n",
    "        # This is the new piece allowing to sample from each component of the mixture\n",
    "        comp = self.prior.component_distribution\n",
    "        z = comp.sample((batchSize,))\n",
    "        z0 = z[:,0,:]\n",
    "        z1 = z[:,1,:]\n",
    "        x0 = self.g(z0)\n",
    "        x1 = self.g(z1)\n",
    "        return x0,x1,z0,z1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J6YPmM_3bTTE"
   },
   "outputs": [],
   "source": [
    "flow = RealNVP(nets, nett, masks, prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that a flow is invertible g(f(x)) = x Hint: torch.allclose\n",
    "x = torch.randn((10,2))\n",
    "y, _ = flow.f(flow.g(x))\n",
    "torch.allclose(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vb80JOSSbTTG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 0: loss = 5.925\n",
      "iter 500: loss = 0.657\n",
      "iter 1000: loss = 0.471\n",
      "iter 1500: loss = 0.480\n",
      "iter 2000: loss = 0.463\n",
      "iter 2500: loss = 0.363\n",
      "iter 3000: loss = 0.290\n",
      "iter 3500: loss = 0.455\n",
      "iter 4000: loss = 0.521\n",
      "iter 4500: loss = 0.383\n",
      "iter 5000: loss = 0.407\n"
     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.Adam(flow.parameters(), lr=1e-4)# choose an optimizer, use module torch.optim\n",
    "\n",
    "for t in range(5001):    \n",
    "    noisy_moons = datasets.make_moons(n_samples=100, noise=.05)[0].astype(np.float32)\n",
    "    loss = -flow.log_prob(torch.from_numpy(noisy_moons)).mean()\n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    if t % 500 == 0:\n",
    "        print('iter %s:' % t, 'loss = %.3f' % loss)\n",
    "        \n",
    "# Check that the loss decreases\n",
    "# Is the visualization below good?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6hOxCpXYbTTJ"
   },
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RmK9g7CIbTTK"
   },
   "outputs": [],
   "source": [
    "rcParams['figure.figsize'] = 10, 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gz5YG8FNbTTN"
   },
   "source": [
    "Draw several plots: \n",
    "- samples from flow\n",
    "- samples from prior\n",
    "- data samples\n",
    "- mapping form data to prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qbhwTtHjpHC9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$X = g(z)$')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noisy_moons = datasets.make_moons(n_samples=1000, noise=.05)[0].astype(np.float32)\n",
    "z = flow.f(torch.from_numpy(noisy_moons))[0].detach().numpy()\n",
    "plt.subplot(221)\n",
    "plt.scatter(z[:, 0], z[:, 1])\n",
    "plt.title(r'$z = f(X)$')\n",
    "\n",
    "\n",
    "x0,x1,z0,z1 = flow.sample_cond(1000)\n",
    "x0 = x0.detach().numpy()\n",
    "x1 = x1.detach().numpy()\n",
    "z0 = z0.detach().numpy()\n",
    "z1 = z1.detach().numpy()\n",
    "\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.scatter(z0[:, 0], z0[:, 1], c='b')\n",
    "plt.scatter(z1[:, 0], z1[:, 1], c='r')\n",
    "plt.title(r'$z \\sim p(z)$')\n",
    "\n",
    "plt.subplot(223)\n",
    "x = noisy_moons #datasets.make_moons(n_samples=1000, noise=.05)[0].astype(np.float32)\n",
    "plt.scatter(x[:, 0], x[:, 1])\n",
    "plt.title(r'$X \\sim p(X)$')\n",
    "\n",
    "plt.subplot(224)\n",
    "#x = flow.sample(1000).detach().numpy()\n",
    "plt.scatter(x0[:, 0], x0[:, 1], c='b')\n",
    "plt.scatter(x1[:, 0], x1[:, 1], c='r')\n",
    "plt.title(r'$X = g(z)$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Dataflowr](https://raw.githubusercontent.com/dataflowr/website/master/_assets/dataflowr_logo.png)](https://dataflowr.github.io/website/)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Normalizing_flows_empty.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "dldiy",
   "language": "python",
   "name": "dldiy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
